Safety-oriented dynamic speed harmonization of mixed traffic flow in nonrecurrent congestion

Abstract

Freeway nonrecurrent congestion adversely affects collision risks, travel time, emissions, and fuel consumption. Optimizing mixed flow involving Human-Driven Vehicles (HDVs) and Connected Automated vehicles (CAVs) is expected to exist as a challenging issue for a long time prior to full adoption of CAVs. To address this, a safety-oriented Dynamic Speed Harmonization (DSH) strategy for mixed traffic flow in nonrecurrent congestion is proposed in this study. This research utilizes a Deep Reinforcement Learning (DRL) framework to investigate the effects of DSH on the measures of effectiveness in microscopic simulation. A holistic evaluation of HDVs and CAVs integrated with DSH highlights the synergies and trade-offs across different metrics. The results reveal that the implementation of DSH can further improve safety and enhance mobility with increasing CAV penetration rates. While special events may exacerbate congestion, their impact can be mitigated to some extent through speed controls. Spatiotemporal patterns of speed variations at the bottleneck demonstrate that the DRL controller has the capability to dampen oscillations and smoothen traffic flow. A series of sensitivity analyses also indicate the adaptability of the agent under adverse weather scenarios, and the differences of surrogate safety measurements in response to various parametric thresholds.